False starts: What the UK’s growing NEETs problem really looks like, and how to fix it
Bibliographic record
Abstract
Nearly one million young people aged 16-24 in the UK are currently not in education, employment or training (NEET) – the highest level in over a decade. While the Government’s new Youth Guarantee marks a welcome step in the right direction, a more ambitious policy agenda that helps all young people to re-engage with education or enter sustainable employment is needed. The NEET population is changing. Most NEETs are now economically inactive rather than unemployed, with rising numbers citing health problems or ‘other’ reasons for not working or studying. More than a quarter of all NEETs are inactive due to sickness or disability, and nearly half are not claiming benefits – meaning they are unlikely to be reached by Jobcentre-based programmes. NEET rates are highest among those with low qualifications, with six-in-ten never having had a paid job, underscoring the deep barriers many face in finding education or employment opportunities. To reverse these trends, the Government must enforce participation requirements for 16-17-year-olds more effectively; and expand the Youth Guarantee to cover 22-24-year-olds as well as 18-21-year-olds and include those not receiving out-of-work benefits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".